Welcome to the InfoSci Platform
Reference Hub4
Creating Risk-Scores in Very Imbalanced Datasets: Predicting Extremely Violent Crime among Criminal Offenders Following Release from Prison

Creating Risk-Scores in Very Imbalanced Datasets: Predicting Extremely Violent Crime among Criminal Offenders Following Release from Prison

Markus Breitenbach, William Dieterich, Tim Brennan, Adrian Fan
Copyright: © 2010 |Pages: 24
ISBN13: 9781605667546|ISBN10: 1605667544|ISBN13 Softcover: 9781616924508|EISBN13: 9781605667553
DOI: 10.4018/978-1-60566-754-6.ch015
Cite Chapter Cite Chapter

MLA

Breitenbach, Markus, et al. "Creating Risk-Scores in Very Imbalanced Datasets: Predicting Extremely Violent Crime among Criminal Offenders Following Release from Prison." Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection, edited by Yun Sing Koh and Nathan Rountree, IGI Global, 2010, pp. 231-254. https://doi.org/10.4018/978-1-60566-754-6.ch015

APA

Breitenbach, M., Dieterich, W., Brennan, T., & Fan, A. (2010). Creating Risk-Scores in Very Imbalanced Datasets: Predicting Extremely Violent Crime among Criminal Offenders Following Release from Prison. In Y. Koh & N. Rountree (Eds.), Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection (pp. 231-254). IGI Global. https://doi.org/10.4018/978-1-60566-754-6.ch015

Chicago

Breitenbach, Markus, et al. "Creating Risk-Scores in Very Imbalanced Datasets: Predicting Extremely Violent Crime among Criminal Offenders Following Release from Prison." In Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection, edited by Yun Sing Koh and Nathan Rountree, 231-254. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-754-6.ch015

Export Reference

Mendeley
Favorite

Abstract

In this chapter, the authors explore Area under Curve (AUC) as an error-metric suitable for imbalanced data, as well as survey methods of optimizing this metric directly. We also address the issue of cut-point thresholds for practical decision-making. The techniques will be illustrated by a study that examines predictive rule development and validation procedures for establishing risk levels for violent felony crimes committed when criminal offenders are released from prison in the USA. The “violent felony” category was selected as the key outcome since these crimes are a major public safety concern, have a low base-rate (around 7%), and represent the most extreme forms of violence. The authors compare the performance of different algorithms on the dataset and validate using survival analysis whether the risk scores produced by these techniques are computing reasonable estimates of the true risk.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.